Adaptive estimation algorithm of SOC based on the internal resistance online identification method

2014 ◽  
Author(s):  
Yuanbin Yu ◽  
Wei Wang ◽  
Wenqiang Lv ◽  
Kai Peng
Keyword(s):  
Adaptive Estimation ◽  
Estimation Algorithm ◽  
Internal Resistance ◽  
Online Identification ◽  
Identification Method

scholarly journals Online diagnosis of supercapacitors using extended Kalman filter combined with PID corrector

2021 ◽  
Vol 12 (3) ◽  
pp. 1521
Author(s):  
Zoubida Bououchma ◽  
Jalal Sabor
Keyword(s):  
Electrochemical Impedance ◽  
New Method ◽  
Specific Power ◽  
Attractive Alternative ◽  
State Of Health ◽  
Energy Storage Devices ◽  
Good Efficiency ◽  
Storage Devices ◽  
Online Identification ◽  
Identification Method

<span>Supercapacitors are electrical energy storage devices with a high specific power density, a long cycle life and a good efficiency, which make them attractive alternative storage devices for various applications. However, supercapacitors are subject to a progressive degradation of their perfor-mance because of aging phenomenon. Therefore, it is very important to be able to estimate their State-of-Health during operation. Electrochemical Impedance Spectroscopy (EIS) is a very recog-nized technique to determine supercapacitors’ state-of-health. However, it requires the interrup-tion of system operation and thus cannot be performed in real time (online). In this paper, a new online identification method is proposed based on extended Kalman observer combined with a complementary PID corrector. The proposed method allows to accurately estimating supercapacitor resistance and capacitance, which are the main indicators of supercapacitor state-of-health. The new online identification method was applied for two voltage/current profiles using two different supercapacitors. The resistance/capacitance estimated by the new method and the conventional EKF were compared with those obtained by an experimental offline method. In comparison with conventional EKF, the capacitance obtained by the new method is significantly more accurate.</span>

Sensor and Actuator Intrusion Detection for Cyber-Physical Systems via Adaptive Estimation Algorithm

2020 ◽  
Author(s):  
Jiayi Su ◽  
Yuqin Weng ◽  
Susan C. Schneider ◽  
Edwin E. Yaz
Keyword(s):  
Kalman Filters ◽  
Adaptive Estimation ◽  
Estimation Algorithm ◽  
Cyber Physical Systems ◽  
Motor Speed ◽  
New Approach ◽  
Physical Systems ◽  
Unknown Type ◽  
Different Types ◽  
Conditional State

Abstract In this work, a new approach to detect sensor and actuator intrusion for Cyber-Physical Systems using a bank of Kalman filters is presented. The case where the unknown type of the intrusion signal is considered first, using two Kalman filters in a bank to provide the conditional state estimates, then the unknown type of intrusion signal can be detected properly via the adaptive estimation algorithm. The case where the target (either sensor or actuator) of the intrusion signal is unknown is also considered, using four Kalman filters in a bank designed to detect if the intrusion signal is about to affect healthy sensor or actuator signal. To test these methods, a DC motor speed control system subject to attack by different types of sensor and actuator signals is simulated. Simulations show that different types of sensor and actuator intrusion signals can be detected properly without the knowledge of the nature and the type of these signals.

Adaptive Observer of Rotor Position and Flux for Salient Synchronous Motor

2019 ◽  
Vol 20 (2) ◽  
pp. 114-121
Author(s):  
D. N. Bazylev ◽  
A. A. Pyrkin ◽  
A. A. Bobtsov
Keyword(s):  
Permanent Magnets ◽  
Adaptive Estimation ◽  
Angular Position ◽  
Regression Function ◽  
Estimation Algorithm ◽  
Synchronous Motor ◽  
Position Estimation ◽  
Adaptive Observer ◽  
Estimation Errors ◽  
Persistent Excitation

An algorithm of adaptive estimation of rotor flux and angular position for the salient synchronous motor with permanent magnets is presented. A new nonlinear parameterization of the dynamic motor model is proposed. Due to this parameterization the problem of position estimation is translated to the task of identification of unknown constant parameters. During the synthesis of estimation algorithm the currents and voltages of the stator windings, as well as the rotor speed, are assumed to be known signals. Two variants of the adaptive observer based on the standard gradient estimator and the algorithm of the dynamic extension of the regressor are proposed. It is proved that the both versions of the observer provide global exponential convergence of estimation errors to zero if the corresponding regression function satisfies the persistent excitation condition. Also, the latter version of the observer provides global asymptotic convergence if the regression function is square integrable. The results of numerical simulation demonstrate that the algorithm with the dynamic extension of the regressor provides a better quality of estimation transient processes in comparison with the standard gradient estimator.

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